@MISC{wiki:ph,
    AUTHOR = {{Wikipedia contributors}"},
    TITLE = "Proportional hazards model --- {Wikipedia}{,} The Free Encyclopedia",
    URL = "https://en.wikipedia.org/w/index.php?title=Proportional_hazards_model&oldid=885553852",
    NOTE = "[Online; accessed 6-March-2019]",
    YEAR = "2019",
}
@MISC{skipper_seabold_2017_275519,
    AUTHOR = {{Skipper Seabold et al.}},
    TITLE = {statsmodels/statsmodels: Version 0.8.0 Release},
    URL = {https://doi.org/10.5281/zenodo.275519},
    DOI = {10.5281/zenodo.275519},
    YEAR = 2017,
    MONTH = feb,
}
@MISC{nathaniel_j_smith_2018_1472929,
    AUTHOR = {{Nathaniel J. Smith et al.}},
    TITLE = {pydata/patsy: v0.5.1},
    URL = {https://doi.org/10.5281/zenodo.1472929},
    DOI = {10.5281/zenodo.1472929},
    YEAR = 2018,
    MONTH = oct,
}
@BOOK{book,
    AUTHOR = {Steven P. Millard, Nagaraj K. Neerchal},
    TITLE = {Environmental Statistics with R},
    PUBLISHER = {Taylor & Francis Inc},
    ISBN = {1439810273},
    EDITION = 2,
    YEAR = 2021,
}
@BOOK{book:xxx,
    AUTHOR = {Steven P. Millard, Nagaraj K. Neerchal},
    TITLE = {Environmental Statistics with R},
    VOLUME = {77},
    NUMBER = {3},
    PAGES = {463-464},
    URL = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1751-5823.2009.00095_1.x},
    YEAR = {2009},
    EPRINT = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1751-5823.2009.00095_1.x},
    DOI = {10.1111/j.1751-5823.2009.00095\_1.x},
    JOURNAL = {International Statistical Review},
}
@MISC{wiki:xxx,
    AUTHOR = {{Wikipedia contributors}"},
    TITLE = "Newton's method --- {Wikipedia}{,} The Free Encyclopedia",
    URL = "https://en.wikipedia.org/w/index.php?title=Newton%27s_method&oldid=884906576",
    NOTE = "[Online; accessed 6-March-2019]",
    YEAR = "2019",
}
@ARTICLE{doi:10.1111/ajps.12176,
    AUTHOR = {Park, Sunhee AND Hendry, David J.},
    TITLE = {Reassessing Schoenfeld Residual Tests of Proportional Hazards in Political Science Event History Analyses},
    VOLUME = {59},
    NUMBER = {4},
    PAGES = {1072-1087},
    URL = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ajps.12176},
    YEAR = {2015},
    ABSTRACT = {An underlying assumption of proportional hazards models is that the effect of a change in a covariate on the hazard rate of event occurrence is constant over time. For scholars using the Cox model, a Schoenfeld residual-based test has become the disciplinary standard for detecting violations of this assumption. However, using this test requires researchers to make a choice about a transformation of the time scale. In practice, this choice has largely consisted of arbitrary decisions made without justification. Using replications and simulations, we demonstrate that the decision about time transformations can have profound implications for the conclusions reached. In particular, we show that researchers can make far more informed decisions by paying closer attention to the presence of outlier survival times and levels of censoring in their data. We suggest a new standard for best practices in Cox diagnostics that buttresses the current standard with in-depth exploratory data analysis.},
    EPRINT = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/ajps.12176},
    DOI = {10.1111/ajps.12176},
    JOURNAL = {American Journal of Political Science},
}
@ARTICLE{doi:10.1002/sim.2864,
    AUTHOR = {Klein, John P. AND Logan, Brent AND Harhoff, Mette AND Andersen, Per Kragh},
    TITLE = {Analyzing survival curves at a fixed point in time},
    VOLUME = {26},
    NUMBER = {24},
    PAGES = {4505-4519},
    URL = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.2864},
    YEAR = {2007},
    ABSTRACT = {Abstract A common problem encountered in many medical applications is the comparison of survival curves. Often, rather than comparison of the entire survival curves, interest is focused on the comparison at a fixed point in time. In most cases, the naive test based on a difference in the estimates of survival is used for this comparison. In this note, we examine the performance of alternatives to the naive test. These include tests based on a number of transformations of the survival function and a test based on a generalized linear model for pseudo-observations. The type I errors and power of these tests for a variety of sample sizes are compared by a Monte Carlo study. We also discuss how these tests may be extended to situations where the data are stratified. The pseudo-value approach is also applicable in more detailed regression analysis of the survival probability at a fixed point in time. The methods are illustrated on a study comparing survival for autologous and allogeneic bone marrow transplants. Copyright © 2007 John Wiley \& Sons, Ltd.},
    EPRINT = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.2864},
    DOI = {10.1002/sim.2864},
    KEYWORDS = {generalized linear models, pseudo-value approach, variance stabilizing transformation, Kaplan–Meier estimators, censored data},
    JOURNAL = {Statistics in Medicine},
}
@ARTICLE{Greenland2016,
    AUTHOR = {Greenland, Douglas G.},
    TITLE = "Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations",
    VOLUME = "31",
    NUMBER = "4",
    PAGES = "337--350",
    URL = "https://doi.org/10.1007/s10654-016-0149-3",
    DOI = "10.1007/s10654-016-0149-3",
    ISSN = "1573-7284",
    ABSTRACT = "Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so---and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.",
    DAY = "01",
    MONTH = "Apr",
    YEAR = "2016",
    JOURNAL = "European Journal of Epidemiology",
}
@MISC{300620,
    AUTHOR = {{Tom Pinder (https://stats.stackexchange.com/users/134199/tom-pinder)}},
    TITLE = {What is the difference between the different types of residuals in survival analysis (Cox regression)?},
    URL = {https://stats.stackexchange.com/q/300620},
    EPRINT = {https://stats.stackexchange.com/q/300620},
    NOTE = {URL:https://stats.stackexchange.com/q/300620 (version: 2017-08-30)},
    HOWPUBLISHED = {Cross Validated},
}
@MISC{46536,
    AUTHOR = {{ocram (https://stats.stackexchange.com/users/3019/ocram)}},
    TITLE = {Cox baseline hazard},
    URL = {https://stats.stackexchange.com/q/46536},
    EPRINT = {https://stats.stackexchange.com/q/46536},
    NOTE = {URL:https://stats.stackexchange.com/q/46536 (version: 2017-03-22)},
    HOWPUBLISHED = {Cross Validated},
}
@MISC{133822,
    AUTHOR = {{Ben Kuhn (https://stats.stackexchange.com/users/60642/ben-kuhn)}},
    TITLE = {Stratified concordance index (survival::survConcordance)},
    URL = {https://stats.stackexchange.com/q/133822},
    EPRINT = {https://stats.stackexchange.com/q/133822},
    NOTE = {URL:https://stats.stackexchange.com/q/133822 (version: 2015-01-17)},
    HOWPUBLISHED = {Cross Validated},
}
@ARTICLE{doi:10.1111/j.1751-5823.2009.00095_1.x,
    AUTHOR = {Ghosh, Jayanta K.},
    TITLE = {Survival and Event History Analysis: A Process Point of View by Odd O. Aalen, Ørnulf Borgan, Håkon K. Gjessing},
    VOLUME = {77},
    NUMBER = {3},
    PAGES = {463-464},
    URL = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1751-5823.2009.00095_1.x},
    YEAR = {2009},
    EPRINT = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1751-5823.2009.00095_1.x},
    DOI = {10.1111/j.1751-5823.2009.00095\_1.x},
    JOURNAL = {International Statistical Review},
}
@ARTICLE{Edwards2016,
    AUTHOR = {Edwards, Catherine R.},
    TITLE = "Methodologic Issues when Estimating Risks in Pharmacoepidemiology",
    VOLUME = "3",
    NUMBER = "4",
    PAGES = "285--296",
    URL = "https://doi.org/10.1007/s40471-016-0089-1",
    DOI = "10.1007/s40471-016-0089-1",
    ISSN = "2196-2995",
    ABSTRACT = "Risk is an important parameter to describe the occurrence of health outcomes over time. However, many outcomes of interest in healthcare settings, such as disease incidence, treatment initiation, and cause-specific mortality, may be precluded from occurring by other events, often referred to as competing events. Here, we review straightforward approaches to estimate risk in the presence of competing events.",
    DAY = "01",
    MONTH = "Dec",
    YEAR = "2016",
    JOURNAL = "Current Epidemiology Reports",
}
@ARTICLE{Harrell1996MultivariablePM,
    AUTHOR = {Frank E. Harrell AND Kerry L. Lee AND Daniel B. Mark},
    TITLE = {Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.},
    VOLUME = {15 4},
    PAGES = {
          361-87
        },
    YEAR = {1996},
    JOURNAL = {Statistics in medicine},
    DOI = "https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4",
}
@ARTICLE{flexsurv,
    AUTHOR = {{Christopher Jackson}},
    TITLE = {{flexsurv}: A Platform for Parametric Survival Modeling in
    {R}},
    VOLUME = {70},
    NUMBER = {8},
    PAGES = {1--33},
    DOI = {10.18637/jss.v070.i08},
    YEAR = {2016},
    JOURNAL = {Journal of Statistical Software},
}
@MANUAL{survival-package,
    AUTHOR = {{Terry M Therneau}},
    TITLE = {A Package for Survival Analysis in S},
    URL = {https://CRAN.R-project.org/package=survival},
    NOTE = {version 2.38},
    YEAR = {2015},
}
